We focused to visualize the impact of different OCC methods with NSA variations, and to best visualize, we took shape data-set. Inside the shapes is the true class, and outside the shape would be the false class. We have 2000 data points inside the shape class. We have another data-set containing 2000 data-point inside and outside shapes for the test. We experimented with two different shapes; Ring and Pentagon. There is inside negative and outside the positive region in the ring, and for the pentagon, outside detection is not linearly separable. In figure REF  illustrated the inlier and outlier position for Ring and Pentagon test and train data. This nature of the shape made it possible to experiment and better visualize. For conducting experiments, we used python pyod library [1]}, and for NSA variations, we used Zhou-Ji's developed code[2]}. Also used the default configuration of pyod library, and for NSA methods, considered minkowski distance. Our experimental platform was google co-lab with GPU and high ram usage enabled. Also, we used open-source repository of data and source code for reproducibility. For CBLOF, the Ring dataset was not considered. We used pyod built in visualization utility and orange tool[3]} for visual image generation.
